knitr::opts_chunk$set(echo = FALSE,cache = TRUE)
library(xlsx)
library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
library(gridExtra)
library(corrplot)
## corrplot 0.84 loaded
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
##
## combine
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(png)
library(grid)
library(heatmaply)
## Loading required package: plotly
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
## Loading required package: viridis
## Loading required package: viridisLite
## Registered S3 method overwritten by 'seriation':
## method from
## reorder.hclust gclus
##
## ======================
## Welcome to heatmaply version 0.16.0
##
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
##
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## Or contact: <tal.galili@gmail.com>
## ======================
## Warning: NAs introduced by coercion
## Tree.ID Allocation Column Row Rep. measure Height Flower Flower.Level
## 840 IN4E4 F1 2 20 N 7 M N 0
## 1363 IN4FM F1 3 17 N 11 H N 0
## Chl Flav Anth Height08 Height09
## 840 18.511 1.401 0.370 138 234
## 1363 59.122 1.640 0.483 166 245
## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing missing values (geom_point).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 21 rows containing non-finite values (stat_bin).
## Warning: Removed 21 rows containing non-finite values (stat_density).
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 0.26483 0.0016762 0.9733 0.5779
## Residuals 1631 2.80887 0.0017222
## [1] 0.08616099
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing missing values (geom_point).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 21 rows containing non-finite values (stat_bin).
## Warning: Removed 21 rows containing non-finite values (stat_density).
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 23815 150.73 0.6775 0.999
## Residuals 1631 362856 222.47
## [1] 0.06158903
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing missing values (geom_point).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 21 rows containing non-finite values (stat_bin).
## Warning: Removed 21 rows containing non-finite values (stat_density).
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 14.876 0.094149 1.8118 1.856e-08 ***
## Residuals 1631 84.754 0.051965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.149308
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing missing values (geom_point).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 8 rows containing non-finite values (stat_bin).
## Warning: Removed 8 rows containing non-finite values (stat_density).
## Warning in anova.lm(mod181): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 171 418774 2449 3.9594e+27 < 2.2e-16 ***
## Residuals 1550 0 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: not plotting observations with leverage one:
## 306, 1600, 1609, 1610, 1611, 1614, 1617, 1622, 1630, 1642, 1644, 1653, 1661, 1680, 1681, 1687, 1690, 1698, 1722
## Warning: not plotting observations with leverage one:
## 306, 1600, 1609, 1610, 1611, 1614, 1617, 1622, 1630, 1642, 1644, 1653, 1661, 1680, 1681, 1687, 1690, 1698, 1722
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## [1] 1
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (geom_point).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 9 rows containing non-finite values (stat_bin).
## Warning: Removed 9 rows containing non-finite values (stat_density).
## Warning in anova.lm(mod191): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
##
## Response: F1$H195
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 170 429289 2525.2 3.72e+27 < 2.2e-16 ***
## Residuals 1550 0 0.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: not plotting observations with leverage one:
## 1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721
## Warning: not plotting observations with leverage one:
## 1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## [1] 1
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (geom_point).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 9 rows containing non-finite values (stat_bin).
## Warning: Removed 9 rows containing non-finite values (stat_density).
## Warning in anova.lm(modHD1): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
##
## Response: F1$HDiff
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 170 382107 2247.7 6.3777e+27 < 2.2e-16 ***
## Residuals 1550 0 0.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: not plotting observations with leverage one:
## 1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721
## Warning: not plotting observations with leverage one:
## 1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## [1] 1
## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.04916 0.0245811 14.523 5.537e-07 ***
## Residuals 1787 3.02454 0.0016925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.0159945
## Analysis of Variance Table
##
## Response: HH$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## HM$Anth 1 0.000215 0.00021465 0.2884 0.592
## Residuals 157 0.116846 0.00074425
## [1] 0.001833689
## Analysis of Variance Table
##
## Response: HL$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## HM$Anth 1 0.000172 0.00017166 0.3687 0.5446
## Residuals 157 0.073097 0.00046558
## [1] 0.002342859
## Analysis of Variance Table
##
## Response: HH$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## HL$Anth 1 0.000118 0.00011794 0.1583 0.6912
## Residuals 157 0.116943 0.00074486
## [1] 0.001007545
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 791 395.31 1.8307 0.1606
## Residuals 1787 385880 215.94
## [1] 0.0159945
## Analysis of Variance Table
##
## Response: HH$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## HM$Chl 1 504.5 504.50 5.982 0.01556 *
## Residuals 157 13240.8 84.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.001833689
## Analysis of Variance Table
##
## Response: HL$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## HM$Chl 1 181.3 181.326 3.2087 0.07518 .
## Residuals 157 8872.3 56.512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.002342859
## Analysis of Variance Table
##
## Response: HH$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## HL$Chl 1 630.2 630.24 7.5446 0.006723 **
## Residuals 157 13115.0 83.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.001007545
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.972 0.48589 8.8009 0.0001572 ***
## Residuals 1787 98.658 0.05521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.009753874
## Analysis of Variance Table
##
## Response: HH$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## HM$Flav 1 0.0540 0.053997 3.1493 0.0779 .
## Residuals 157 2.6919 0.017146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.001833689
## Analysis of Variance Table
##
## Response: HL$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## HM$Flav 1 0.1518 0.151825 5.739 0.01777 *
## Residuals 157 4.1534 0.026455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.002342859
## Analysis of Variance Table
##
## Response: HH$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## HL$Flav 1 0.00651 0.0065076 0.373 0.5423
## Residuals 157 2.73937 0.0174482
## [1] 0.001007545
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 0.00086 0.0000176 0.01 1
## Residuals 1740 3.07284 0.0017660
## [1] 0.000280517
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 48 0.977 0.0044 1
## Residuals 1740 386623 222.197
## [1] 0.0001237819
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 0.204 0.004166 0.0729 1
## Residuals 1740 99.426 0.057141
## [1] 0.002048742
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 24288 495.68 2.1009 1.659e-05 ***
## Residuals 1672 394486 235.94
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.05799867
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Analysis of Variance Table
##
## Response: F1$H195
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 36836 751.76 3.2009 1.777e-12 ***
## Residuals 1671 392453 234.86
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.08580726
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Analysis of Variance Table
##
## Response: F1$HDiff
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 2266 46.25 0.2035 1
## Residuals 1671 379841 227.31
+R.squared
## [1] 0.00593089
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 0.00038 0.00012737 0.074 0.9739
## Residuals 1786 3.07332 0.00172078
## [1] 0.0001243198
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 198 65.844 0.3043 0.8223
## Residuals 1786 386473 216.390
## [1] 0.0005108494
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 0.012 0.004060 0.0728 0.9746
## Residuals 1786 99.618 0.055777
## [1] 0.0001222632
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 15817 5272.4 22.479 2.826e-14 ***
## Residuals 1718 402957 234.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.03776989
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$H195
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 23664 7887.8 33.389 < 2.2e-16 ***
## Residuals 1717 405626 236.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.05512251
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$HDiff
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 1958 652.75 2.9483 0.03172 *
## Residuals 1717 380149 221.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.005124903
Flowering refers to presence or not of flowers, Flower Level refers to a measure relating to the number of flowers present (0 = no flowers, 1 = 1-10, 2 = 10-20, 3 = 20+)
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower.Level 3 0.0028 0.00093363 0.543 0.6529
## Residuals 1786 3.0709 0.00171943
## [1] 0.0009112453
##
## Welch Two Sample t-test
##
## data: FLWR0$Anth and FLWR1$Anth
## t = 0.025664, df = 17.375, p-value = 0.9798
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.008423762 0.008631570
## sample estimates:
## mean of x mean of y
## -0.0001864908 -0.0002903948
##
## Welch Two Sample t-test
##
## data: FLWR0$Anth and FLWR2$Anth
## t = 0.2922, df = 10.049, p-value = 0.7761
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.006353989 0.008273500
## sample estimates:
## mean of x mean of y
## -0.0001864908 -0.0011462463
##
## Welch Two Sample t-test
##
## data: FLWR0$Anth and FLWR3$Anth
## t = -1.1298, df = 10.172, p-value = 0.2845
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.014848706 0.004841807
## sample estimates:
## mean of x mean of y
## -0.0001864908 0.0048169586
##
## Welch Two Sample t-test
##
## data: FLWR1$Anth and FLWR2$Anth
## t = 0.1717, df = 22.843, p-value = 0.8652
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.009459314 0.011171017
## sample estimates:
## mean of x mean of y
## -0.0002903948 -0.0011462463
##
## Welch Two Sample t-test
##
## data: FLWR1$Anth and FLWR3$Anth
## t = -0.88018, df = 21.286, p-value = 0.3886
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.017164673 0.006949967
## sample estimates:
## mean of x mean of y
## -0.0002903948 0.0048169586
##
## Welch Two Sample t-test
##
## data: FLWR2$Anth and FLWR3$Anth
## t = -1.1256, df = 15.954, p-value = 0.277
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.017196954 0.005270544
## sample estimates:
## mean of x mean of y
## -0.001146246 0.004816959
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower 1 0.00039 0.00039351 0.2289 0.6324
## Residuals 1788 3.07330 0.00171885
## [1] 0.0001280242
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower.Level 3 1461 487.00 2.2579 0.07985 .
## Residuals 1786 385210 215.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.003778413
##
## Welch Two Sample t-test
##
## data: FLWR0$Chl and FLWR1$Chl
## t = -1.3501, df = 18.737, p-value = 0.1931
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.4526591 0.7464858
## sample estimates:
## mean of x mean of y
## -0.2203429 1.1327437
##
## Welch Two Sample t-test
##
## data: FLWR0$Chl and FLWR2$Chl
## t = -3.0493, df = 9.63, p-value = 0.01279
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.7911270 -0.8863768
## sample estimates:
## mean of x mean of y
## -0.2203429 3.1184090
##
## Welch Two Sample t-test
##
## data: FLWR0$Chl and FLWR3$Chl
## t = 0.70751, df = 10.428, p-value = 0.4948
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.844508 3.574981
## sample estimates:
## mean of x mean of y
## -0.2203429 -1.0855790
##
## Welch Two Sample t-test
##
## data: FLWR1$Chl and FLWR2$Chl
## t = -1.4075, df = 19.522, p-value = 0.175
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.9330444 0.9617139
## sample estimates:
## mean of x mean of y
## 1.132744 3.118409
##
## Welch Two Sample t-test
##
## data: FLWR1$Chl and FLWR3$Chl
## t = 1.4669, df = 19.505, p-value = 0.1583
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9413134 5.3779589
## sample estimates:
## mean of x mean of y
## 1.132744 -1.085579
##
## Welch Two Sample t-test
##
## data: FLWR2$Chl and FLWR3$Chl
## t = 2.6688, df = 16.937, p-value = 0.01623
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.8795891 7.5283869
## sample estimates:
## mean of x mean of y
## 3.118409 -1.085579
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower 1 19 18.847 0.0872 0.7679
## Residuals 1788 386652 216.248
## [1] 4.87422e-05
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower.Level 3 0.574 0.191220 3.4477 0.01606 *
## Residuals 1786 99.056 0.055463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.005757915
##
## Welch Two Sample t-test
##
## data: FLWR0$Flav and FLWR1$Flav
## t = -0.19352, df = 20.332, p-value = 0.8485
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.04944842 0.04104444
## sample estimates:
## mean of x mean of y
## -0.0047609014 -0.0005589119
##
## Welch Two Sample t-test
##
## data: FLWR0$Flav and FLWR2$Flav
## t = -2.2376, df = 8.9795, p-value = 0.05211
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1555838602 0.0008748158
## sample estimates:
## mean of x mean of y
## -0.004760901 0.072593621
##
## Welch Two Sample t-test
##
## data: FLWR0$Flav and FLWR3$Flav
## t = -0.58781, df = 9.9953, p-value = 0.5697
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.10202255 0.05943198
## sample estimates:
## mean of x mean of y
## -0.004760901 0.016534383
##
## Welch Two Sample t-test
##
## data: FLWR1$Flav and FLWR2$Flav
## t = -1.8689, df = 13.816, p-value = 0.08299
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.15721039 0.01090532
## sample estimates:
## mean of x mean of y
## -0.0005589119 0.0725936208
##
## Welch Two Sample t-test
##
## data: FLWR1$Flav and FLWR3$Flav
## t = -0.42086, df = 14.853, p-value = 0.6799
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1037374 0.0695508
## sample estimates:
## mean of x mean of y
## -0.0005589119 0.0165343828
##
## Welch Two Sample t-test
##
## data: FLWR2$Flav and FLWR3$Flav
## t = 1.1508, df = 16.999, p-value = 0.2658
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.04672184 0.15884031
## sample estimates:
## mean of x mean of y
## 0.07259362 0.01653438
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower 1 0.24 0.240050 4.3184 0.03784 *
## Residuals 1788 99.39 0.055587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.002409418
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower.Level 3 11639 3879.6 16.371 1.718e-10 ***
## Residuals 1718 407135 237.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.02779275
##
## Welch Two Sample t-test
##
## data: FLWR0$H18 and FLWR1$H18
## t = -1.6305, df = 17.703, p-value = 0.1207
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -18.48037 2.34066
## sample estimates:
## mean of x mean of y
## -2.410131 5.659726
##
## Welch Two Sample t-test
##
## data: FLWR0$H18 and FLWR2$H18
## t = -0.81929, df = 6.4906, p-value = 0.4417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -22.95103 11.27931
## sample estimates:
## mean of x mean of y
## -2.410131 3.425728
##
## Welch Two Sample t-test
##
## data: FLWR0$H18 and FLWR3$H18
## t = -1.5807, df = 9.3853, p-value = 0.147
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -19.310608 3.365938
## sample estimates:
## mean of x mean of y
## -2.410131 5.562204
##
## Welch Two Sample t-test
##
## data: FLWR1$H18 and FLWR2$H18
## t = 0.26453, df = 11.816, p-value = 0.7959
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.19812 20.66612
## sample estimates:
## mean of x mean of y
## 5.659726 3.425728
##
## Welch Two Sample t-test
##
## data: FLWR1$H18 and FLWR3$H18
## t = 0.014376, df = 20.604, p-value = 0.9887
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -14.02649 14.22153
## sample estimates:
## mean of x mean of y
## 5.659726 5.562204
##
## Welch Two Sample t-test
##
## data: FLWR2$H18 and FLWR3$H18
## t = -0.25133, df = 11.217, p-value = 0.8061
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -20.80233 16.52937
## sample estimates:
## mean of x mean of y
## 3.425728 5.562204
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower 1 2126 2125.89 8.776 0.003094 **
## Residuals 1720 416648 242.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.005076449
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$H195
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower.Level 3 8766 2922.09 11.931 9.858e-08 ***
## Residuals 1717 420523 244.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.02042042
##
## Welch Two Sample t-test
##
## data: FLWR0$H19 and FLWR1$H19
## t = -1.7634, df = 18.363, p-value = 0.09447
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -17.725963 1.535942
## sample estimates:
## mean of x mean of y
## -1.992529 6.102482
##
## Welch Two Sample t-test
##
## data: FLWR0$H19 and FLWR2$H19
## t = -0.12465, df = 6.2813, p-value = 0.9047
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -24.14341 21.77867
## sample estimates:
## mean of x mean of y
## -1.9925285 -0.8101606
##
## Welch Two Sample t-test
##
## data: FLWR0$H19 and FLWR3$H19
## t = 0.3993, df = 9.2524, p-value = 0.6987
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -9.983022 14.284387
## sample estimates:
## mean of x mean of y
## -1.992529 -4.143211
##
## Welch Two Sample t-test
##
## data: FLWR1$H19 and FLWR2$H19
## t = 0.66835, df = 8.7152, p-value = 0.5212
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.60158 30.42687
## sample estimates:
## mean of x mean of y
## 6.1024818 -0.8101606
##
## Welch Two Sample t-test
##
## data: FLWR1$H19 and FLWR3$H19
## t = 1.5106, df = 18.391, p-value = 0.1479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.982633 24.474019
## sample estimates:
## mean of x mean of y
## 6.102482 -4.143211
##
## Welch Two Sample t-test
##
## data: FLWR2$H19 and FLWR3$H19
## t = 0.31093, df = 9.5696, p-value = 0.7625
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -20.69819 27.36429
## sample estimates:
## mean of x mean of y
## -0.8101606 -4.1432112
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$H195
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower 1 343 342.61 1.373 0.2415
## Residuals 1719 428947 249.53
## [1] 0.0007980757
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$HDiff
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower.Level 3 11680 3893.3 18.046 1.569e-11 ***
## Residuals 1717 370428 215.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.0305669
##
## Welch Two Sample t-test
##
## data: FLWR0$HDiff and FLWR1$HDiff
## t = 0.075464, df = 22.089, p-value = 0.9405
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.774658 7.286433
## sample estimates:
## mean of x mean of y
## 0.6986430 0.4427554
##
## Welch Two Sample t-test
##
## data: FLWR0$HDiff and FLWR2$HDiff
## t = 1.4817, df = 9.0037, p-value = 0.1725
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.598586 12.467650
## sample estimates:
## mean of x mean of y
## 0.698643 -4.235889
##
## Welch Two Sample t-test
##
## data: FLWR0$HDiff and FLWR3$HDiff
## t = 2.0331, df = 9.4104, p-value = 0.07121
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.095699 21.903816
## sample estimates:
## mean of x mean of y
## 0.698643 -9.705415
##
## Welch Two Sample t-test
##
## data: FLWR1$HDiff and FLWR2$HDiff
## t = 1.0879, df = 17.464, p-value = 0.2914
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.376308 13.733596
## sample estimates:
## mean of x mean of y
## 0.4427554 -4.2358888
##
## Welch Two Sample t-test
##
## data: FLWR1$HDiff and FLWR3$HDiff
## t = 1.751, df = 14.32, p-value = 0.1013
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.256559 22.552900
## sample estimates:
## mean of x mean of y
## 0.4427554 -9.7054153
##
## Welch Two Sample t-test
##
## data: FLWR2$HDiff and FLWR3$HDiff
## t = 0.94948, df = 12.734, p-value = 0.36
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.001849 17.940902
## sample estimates:
## mean of x mean of y
## -4.235889 -9.705415
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).
## Analysis of Variance Table
##
## Response: F1$HDiff
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower 1 4246 4245.9 19.316 1.176e-05 ***
## Residuals 1719 377862 219.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.01111171
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 90 rows containing non-finite values (stat_smooth).
## Warning: Removed 90 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 90 rows containing non-finite values (stat_smooth).
## Warning: Removed 90 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 90 rows containing non-finite values (stat_smooth).
## Warning: Removed 90 rows containing missing values (geom_point).
### Anthocyanin and Chlorophyll
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Chl5 1 0.33366 0.33366 217.73 < 2.2e-16 ***
## Residuals 1788 2.74003 0.00153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.1085545
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flav5 1 0.08082 0.080820 48.283 5.148e-12 ***
## Residuals 1788 2.99288 0.001674
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.02629407
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H185 1 0.00199 0.0019900 1.1487 0.284
## Residuals 1699 2.94338 0.0017324
## [1] 0.0006756401
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H195 1 0.0000 0.00000073 4e-04 0.9836
## Residuals 1699 2.9454 0.00173359
## [1] 2.490926e-07
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$HDiff 1 0.00209 0.0020945 1.2091 0.2717
## Residuals 1699 2.94327 0.0017324
## [1] 0.0007111262
## Analysis of Variance Table
##
## Response: F1$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flav5 1 0.08082 0.080820 48.283 5.148e-12 ***
## Residuals 1788 2.99288 0.001674
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.02629407
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H185 1 414 413.56 1.9052 0.1677
## Residuals 1699 368794 217.07
## [1] 0.001120119
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H195 1 78 78.221 0.36 0.5486
## Residuals 1699 369130 217.263
## [1] 0.0002118618
## Analysis of Variance Table
##
## Response: F1$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$HDiff 1 142 141.85 0.653 0.4191
## Residuals 1699 369066 217.22
## [1] 0.0003842085
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H185 1 0.073 0.073328 1.3145 0.2517
## Residuals 1699 94.773 0.055782
## [1] 0.0007731187
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H195 1 0.205 0.204977 3.6797 0.05525 .
## Residuals 1699 94.642 0.055704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.002161139
## Analysis of Variance Table
##
## Response: F1$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$HDiff 1 0.582 0.58187 10.487 0.001225 **
## Residuals 1699 94.265 0.05548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.006134827
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$H195 1 125710 125710 740.77 < 2.2e-16 ***
## Residuals 1719 291720 170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.3011528
## Analysis of Variance Table
##
## Response: F1$H185
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$HDiff 1 89689 89689 470.42 < 2.2e-16 ***
## Residuals 1719 327741 191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.2148603
## Analysis of Variance Table
##
## Response: F1$H195
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$HDiff 1 101549 101549 532.62 < 2.2e-16 ***
## Residuals 1719 327741 191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.2148603